30 Questions you’ll get in your next data scientist job interview

You’ve perfected your resume and portfolio, completed all of the steps to the application process, landed an interview with a top company and now, it’s time to prepare for your next data scientist job interview. Knowing what questions may come up can help you best decide ahead of time how to answer when asked in an interview

Preparing for potential interview questions as a data scientist involves understanding exactly why the company is hiring a data scientist. Likely, they are looking for someone with a specific, technical skill set who can analyze their data and help set their business up for success. 

When a company is hiring a data scientist, they’ll often rely on technical skills evaluations, hands-on projects, and questions about your data scientist portfolio in addition to asking common interview questions about a potential hire’s job history, execution of soft skills, and how they may fit within the company culture

Data scientist job interview questions generally fall within one of three categories: Project-specific questions, technical questions, and general questions. Read on for advice on how to answer questions within each of these categories. 

General Questions

Even though you will be asked a variety of project-specific questions and technical questions that relate to data science, an interviewer will generally also ask several common interview questions to determine your behavior, work style, and learn how you communicate

Whether you have a one-on-one interview, or a panel interview, these questions are paramount to the hiring manager understanding where you might fit into their company. While any data scientist might have the technical skills to get the job done, a candidate who best fits within their company culture is more likely to have staying power and be able to produce results long-term. 

Tell me about yourself.

This common opening question is your opportunity to briefly explain your experience, highlight relevant accomplishments, and set the tone for what else the interviewer may ask. A succinct elevator pitch is great, but to avoid delivering a dry or forgettable answer, check out these 10 creative ways to answer the ‘tell me about yourself’ question

Where do you see yourself in five years?

Most interviewers will ask you where you see yourself in five years to gauge whether or not you are looking to stay with their company long-term. Use this as an opportunity to show that you have a growth mindset and touch on what specifically you are looking forward to working on within the company and where you see the potential for growth.

What is your work style?

To answer this question, you’ll need to know what your work style is and how it might fit into the company you’re interviewing for. When preparing for this interview question as a data scientist, focus on explaining how you communicate, your accuracy on the job, your preferred daily structure, and preference for collaboration.

How will your relevant experience help you in this role?

Highlight what you bring to the table from past positions that will help you excel at this particular company as a data scientist. Point to specifics from past projects to help give the interviewer concrete details to back up your experiences. 

Describe your ideal work environment

This is another interview question where you can show that you have done your homework. For example, you can give the interviewer an indication that you are aware of their company’s propensity for collaboration and flexibility by letting them know that you thrive when working in teams with other data scientists and analysts, but are also comfortable working independently when necessary. 

What are your strengths?

Instead of rattling off a list of adjectives that describe your strengths as a data scientist, list three specific qualities and give examples as to why these strengths make you the perfect person for this particular role. 

What are your weaknesses?

To begin answering this interview question, put a positive spin on it by saying something like “I am working to improve on…” or “Some areas where I am working hard to cultivate new skills are…” It’s OK if these answers are technical skills that you’re working to improve upon like data analysis or programming techniques, but you could also point out areas of soft skills that you need to polish such as receiving feedback or constructive criticism

How do you handle conflict?

This question requires honesty. Relay a brief story about a time when you’ve faced conflict in the workplace as a data scientist and the specifics of how you overcame adversity to work through your differences with others. 

What motivates you?

If you don’t already know what motivates you as a data scientist, now is the perfect time to figure that out. You’ll want to address your passion for your work, as well as what drives you to want to stay gainfully employed in the field. This answer will be different for everyone, so it is important to take time to prepare a well-thought out answer to impress your interviewer

Why do you want to work at this company as a data scientist?

This is the perfect opportunity to show that you’ve done your homework. For example, you could explain why your approach to data science aligns with the company’s innovative methods and processes. This will show your passion for being a data scientist as well as your knowledge of their company at the same time. 

Project-specific questions

It is likely that your interviewer has thoroughly reviewed your data science portfolio prior to your meeting. Be prepared to discuss in detail what the project entailed, why you handled it the way that you did, and what the outcome was. This will of course vary widely, but if you are asked to submit a portfolio prior to your interview, it is fair to expect a technical question or two about previous projects.

How to answer: To prepare answers for project-specific data science questions, it is imperative to know the projects listed in your portfolio inside and out. Your answers should be clear, concise, and answer the question that was asked. For example, if your hiring manager asks about technical challenges and how you solved them, it isn’t necessary to go into why you were selected to manage the project. 

The following are examples of project-specific questions that might be asked in a data scientist job interview. Take a look at each question and consider how you might answer it for each specific project in your portfolio. Practice saying your answer out loud so that you won’t fumble over your words during an interview. 

  1. What technical challenges did you face with this project? What helped you overcome them?
  2. How did you choose this project’s programming technique?
  3. Explain the techniques you used to clean this data set.
  4. How long did this project take?
  5. What would you change about your approach to this project if given the opportunity?
  6. Can you explain how this specific section of code works?
  7. Did you work with a team on this project? Why or why not?
  8. Why did you choose this project’s statistical techniques?
  9. What tools did you use to complete this project?
  10. How would you expand upon this project if asked to?

Technical Questions

As a data scientist, knowing how to answer technical questions is also incredibly important. After all, the entirety of your job is dependent upon executing technical skills as they relate to data collection and analysis. 

How to answer: When asked technical questions about data science in a job interview, you’ll likely need to depend upon your training and experience to guide you. To a hiring manager, your ability to answer technical questions with ease shows that you know how to execute the skills necessary to get the job done.

Be upfront and honest about your skill level and expertise. The worst thing you can do in any interview is overstate your ability. This sets you and your interviewer up for failure should you be hired for a job that you actually are not qualified to do. 

The list of questions below are common technical questions you might be asked in a data scientist job interview. Go over each interview question while preparing for your interview and practice explaining your answer in a clear, concise manner. It is also advisable to come up with relevant examples of when you have used certain methods or analytics systems in the past to explain your familiarity. 

  1. Explain what a decision tree is.
  2. What tools or devices help you succeed as a data scientist?
  3. How do you clean up and organize big data sets?
  4. Should you train and test a machine learning model on the same data?
  5. What is cross-validation?
  6. Why would you use Null as a data value?
  7. What is the primary key?
  8. What is a foreign key?
  9. What method do you use to identify outliers within a data set?
  10. How do you identify a barrier to performance?

While data science interviews can be handled a variety of ways, these questions — or at least some version of these questions — could pop up, so it’s best to know ahead of time how you might answer them. Being well-prepared can help you ace your interview and land the job.

Ashley Jones is a frequent contributor to Ladders News.